Two Compacted Models for Efficient Model-Based Diagnosis

Abstract

Model-based diagnosis (MBD) with multiple observations is complicated and difficult to manage over. In this paper, we proposed two new diagnosis models, namely, the Compacted Model with Multiple Observations (CMMO) and the Dominated-based Compacted Model with Multiple Observations (D-CMMO), to solve the problem in which a considerable amount of time is needed when multiple observations are given and more than one fault is injected. Three ideas are presented in this paper. First, we propose to encode MBD with each observation as a subsystem and share as many system variables as possible to compress the size of encoded clauses. Second, we utilize the notion of gate dominance in the CMMO approach to compute Top-Level Diagnosis with Compacted Model (CM-TLD) to reduce the solution space. Finally, we explore the performance of our model using three fault models. Experimental results on the ISCAS-85 benchmarks show that CMMO and D-CMMO perform better than the state-of-the-art algorithms.

How Should I Compute My Candidates? A Taxonomy and Classification of Diagnosis Computation Algorithms

“…[52,Sec. 4]), or whether it is suitable for multi-observation problems (cf., e.g., [71]), (b) empirical viewpoint, e.g., whether an algorithm was experimentally evaluated, or which other methods an algorithm was compared against, (c) presentation viewpoint, e.g., whether formal proofs for algorithm properties are given, or from the (d) pragmatic viewpoint, e.g., whether there are freely accessible implementations of or tools based on an algorithm. Exploring further features like these is a future work topic.…”